Applied AI ML [Multiple Positions Available]

DESCRIPTION:

Duties: Design and develop automation-based systems for data analysis, document management, and client intelligence.

Research machine learning methods for data processing and analysis.

Develop machine learning models to solve real-world problems and apply it to tasks such as Natural Language Processing, speech recognition, time-series predictions, reinforcement learning, and recommendation systems.

Collaborate with multiple partner teams to deploy solutions into production.

Develop large-scale frameworks to accelerate the application of machine learning models across different areas of the business.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Computational Data Science, Computer Science, Electrical Engineering, Mathematics, Operations Research, or Data Science or related field of study plus 1 year of experience in the job offered or as Applied AI ML, Software Engineer, Analyst in Engineering Division, Research and Development Team, or related occupation.

Skills Required: This position requires experience with the following: Developing and deploying production-ready NLP and speech recognition systems; Designing and developing machine learning experiments and frameworks, including data ingestion, augmentation, generation, feature extraction, end-to-end model training, fine-tuning, performance evaluation and monitoring, robustness and integration testing, A/B testing, interpretability analysis, and visualization; Outlining and evaluating intrinsic and extrinsic model performance metrics aligned with business goals, including deriving extrinsic KPIs incorporating cost per model and cost savings from automation; Preparing and analyzing data quality metrics, including completeness and consistency; Using distributed deep learning, data processing, and model training frameworks, including PyTorch, TensorFlow, HuggingFace, Keras, SKlearn, NLTK, spaCy, Rasa NLU, Numpy, Pandas, and Spark; Processing large datasets, training scalable models, and deploying models on multiple CPUs and GPUs using Cloud-Native Managed Services including as AWS, Azure, and GCP; Applying machine learning techniques, including logistic regression, gradient-boosted trees, and deep learning approaches including RNN and CNN to NLP and Speech applications; Employing Elasticsearch inverted-index, BM25 ranking, and custom analyzers; Leveraging pretrained Transformer encoders including BERT, Sentence-Transformers, and spaCy for text understanding and extraction; Performing A/B testing and data-driven product development, including hypothesis and metrics design, randomization, statistical testing, multiple comparisons, error controls, and experiment monitoring; Implementing continuous integration for ML models, including pipeline definition and triggering using Jenkins, GitLab CI/CD, GitHub Actions; Artifact packaging, containerizing and deployment using Docker; Developing unit tests for ML, including module-level test cases, edge-case ...